WO2003071617A2 - Outil diagnostique predictif base sur des modeles pour batteries primaires et secondaires - Google Patents

Outil diagnostique predictif base sur des modeles pour batteries primaires et secondaires Download PDF

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WO2003071617A2
WO2003071617A2 PCT/US2003/004652 US0304652W WO03071617A2 WO 2003071617 A2 WO2003071617 A2 WO 2003071617A2 US 0304652 W US0304652 W US 0304652W WO 03071617 A2 WO03071617 A2 WO 03071617A2
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algorithm
condition parameter
battery
electrochemical cell
prediction
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WO2003071617A3 (fr
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James D. Kozlowksi
Carl S. Byington
Amulya K. Garga
Thomas Cawley
Matthew J. Watson
Todd A. Hay
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The Penn State Research Foundation
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Publication of WO2003071617A3 publication Critical patent/WO2003071617A3/fr

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
    • B60L3/0023—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train
    • B60L3/0046—Detecting, eliminating, remedying or compensating for drive train abnormalities, e.g. failures within the drive train relating to electric energy storage systems, e.g. batteries or capacitors
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M6/00—Primary cells; Manufacture thereof
    • H01M6/50—Methods or arrangements for servicing or maintenance, e.g. for maintaining operating temperature
    • H01M6/5044—Cells or batteries structurally combined with cell condition indicating means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/54—Drive Train control parameters related to batteries
    • B60L2240/545—Temperature
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/54—Drive Train control parameters related to batteries
    • B60L2240/547—Voltage
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/54—Drive Train control parameters related to batteries
    • B60L2240/549—Current
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/44—Control modes by parameter estimation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/50—Control modes by future state prediction
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/24—Energy storage means
    • B60W2510/242—Energy storage means for electrical energy
    • B60W2510/248—Age of storage means
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

Definitions

  • the present invention relates to apparatus and methods relating to determining the condition of electrochemical cells and batteries.
  • a battery is an arrangement of electrochemical cells configured to produce a certain terminal voltage and discharge capacity. Each cell in the battery is comprised of two electrodes where charge transfer reactions occur.
  • the anode is the electrode at which an oxidation (O) reaction occurs.
  • the cathode is the electrode at which a reduction (R) reaction occurs.
  • the electrolyte provides a supply of chemical species required to complete the charge transfer reactions and a medium through which the species (ions) can move between the electrodes.
  • the electrodes are often fabricated with an extended surface area such as an array of thin plates or sintered powder. The connection of such shapes with the terminals is accomplished through the anode and cathode current collectors.
  • the electrodes are usually positioned in very close proximity to reduce ionic conduction path lengths.
  • a separator is generally placed between the electrodes to maintain proper electrode separation despite deposition of corrosion products.
  • Electrode potentials or voltages Different combinations of electroactive species produce different electrode potentials or voltages.
  • the electrochemical reactions that occur at the electrodes can generally be reversed by application of a higher potential that reverses the current through the cell. In situations where the reverse reaction occurs at a lower potential than any collateral reaction, a rechargeable or secondary cell can potentially be produced.
  • a cell that cannot be recharged because of an undesired reaction or an undesirable physical effect of cycling on the electrodes is called a primary cell.
  • the amount of electrical current that a battery can provide is governed by the reaction rates at the electrodes.
  • the four processes that control the reaction rates of the electrodes are: (1) the mass transfer of the ions into the diffusion layer at the electrode surface area, (2) transfer of the electrons at the electrode surface, (3) intermediate reaction steps resulting from the chemical reaction in the diffusion layer and (4) other surface reactions such as adsorption or desorption of species. These processes represent the physical phenomena that occur in the battery.
  • Electrochemical cell processes are affected by a number of internal and external variables. Electrode variables include material, surface area, geometry, and surface conditions. Mass transfer variables include diffusion, convection, surface concentration, and adsorption. Solution variables include bulk concentration of electroactive species, concentration of electrolyte, and solvent used. Electrical variables include potential, current, and charge. External variables include temperature, pressure, and time. Changes in the electrode surface, diffusion layer and solution are not directly observable without tearing the battery cell apart. Other variables such as potential, current and temperature are observable and can be used to indirectly determine the performance of physical processes.
  • the capacity and voltage of a cell are the primary specifications required for an application.
  • the capacity is defined as the time integral of current delivered to a specified load before the terminal voltage drops below a predetermined cut-off voltage.
  • the rated capacity is not strictly determinable but instead represents the statistical properties of test data for identical cells.
  • the present condition of a cell is described nominally with a state of charge (SOC) that is usually defined as the ratio of the remaining capacity and nominal capacity. Obviously, in order to assess SOC, one must have knowledge of the service history of the cell and its nominal capacity. Secondary cells are observed to have a capacity that deteriorates over the service life of the cell.
  • SOC state of charge
  • SOH State of health
  • SOL state of life
  • Models that produce cell or terminal voltage have also been used, for example to simulate the voltage produced under load until the cutoff voltage is reached. These models make a number of assumptions about the system. For example, initial SOC needs to be known, which represents a source for error. Also, aging of the battery is not addressed, which is another source for error. Impedance is not used in these models. Another non-impedance approach is coulomb counting, which simply uses the measured current to establish how much energy is removed for the battery. Again, this assumes accurate knowledge of the initial SOC and compensation for loading and temperature changes.
  • Embodiments of the present invention provide a method for using measured information to determine the condition (including the health) of batteries, other electrochemical cells, and other systems where system properties such as electrical impedance can be correlated with the condition of the system, such as system health, lifetime, remaining life, charge, and the like.
  • Embodiments of the present invention include a battery diagnostic system and battery diagnosis methods, wherein the condition of a battery can be determined.
  • the condition and health of a battery can be defined by three categories of condition parameter: State-of-Charge (SOC), State-of-Health (SOH), and State-of- Life (SOL).
  • SOC State-of-Charge
  • SOH State-of-Health
  • SOL State-of- Life
  • the processed information from this category can be reported in two forms, initial SOC before loading or charging and continuous SOC, which is the most recent measure of stored energy during discharging/charging.
  • SOH is a measure of the physical condition of the underlining processes. For example, SOH may indicate the amount of passivation that has occurred or how much of the electrolyte has evaporated.
  • SOL is a measure of the remaining usable energy.
  • the processed information from this category is reported in two classes, Remaining-Useful-Energy (RUE) and Remaining- Useful-Cycles (RUC).
  • RUE refers to the amount of stored energy remaining in the battery. This energy can refer to energy received from recharging or formation during manufacturing of new batteries.
  • Embodiments of the present invention describe new methods for assessing the condition of batteries, by determination of condition parameters correlated with the condition.
  • a method to accurately assess the state-of-charge (SOC), state-of-health (SOH), and state-of-life (SOL) of primary and secondary batteries can provide significant benefits in operational systems. This method is based on accurate modeling of the transport mechanisms within the battery and requires careful development of electrochemical and thermal models.
  • a novel impedance technique was previously developed to take wideband impedance data from the battery being tested.
  • a feature extraction algorithm was implemented to identify physically meaningful information from the impedance data. These extracted virtual sensor signals (i.e. electrochemical process parameters) are saved along with the impedance data and other measured signal data into a feature vector file.
  • the feature vector file provides input data for prediction algorithms.
  • Three-prong Auto-Regressive Moving Average (ARMA), Neural Network, and Fuzzy Logic algorithms read this file to produce predictions of the SOC, SOH, and SOL.
  • a decision fusion algorithm combines the predictions along with historical and system information to produce a more robust prediction and confidence level. The results of the fusion are then outputted to the user.
  • the training of these algorithms can be achieved using data from lead-acid, nickel-cadmium, and lithium batteries as well as other types of various capacities, which can be run under different load, charging, and temperature conditions.
  • the developed hardware and software can be implemented on both a laboratory test bench and a smaller portable system. These software-supported methods can provide improved diagnostic information about a battery under examination.
  • Embodiments of the present invention may be used in applications such as automotive and small vehicle batteries, electric vehicle systems, and backup power for communication, banking, medical, and computer network systems.
  • the methodology could be used in other applications such as fuel cell diagnostics and online machine oil quality analysis.
  • a measurement signal provides information correlated to the battery condition, such as terminal voltage, load or charge current, one or more temperatures, or a signal correlated with battery impedance.
  • An electrochemical parameter relates to internal electrochemical processes within a battery, such as electrolyte resistance, charge transfer resistances, double-layer capacitances, and diffusion layer impedance coefficients. Electrolyte parameters can relate to the bulk electrolyte, one or more electrode surface regions, or electrodes.
  • a feature vector is a data set determined by information comprising measurement signals, and provides information to one or more prediction algorithms.
  • a prediction algorithm provides a prediction of a battery condition parameter, such as SOC, SOH, and SOL, based on received data, such as feature vectors, and the output of two or more prediction algorithms can be evaluated by a decision fusion algorithm so as to provide an improved prediction of a battery condition parameter, such as state of charge.
  • a decision fusion algorithm provides a prediction of the battery condition parameter based on the predictions of two or more sources of data, such as prediction algorithms.
  • Figure 2 shows a schematic of a model-based predictive diagnostic system
  • Figure 3 illustrates feature extraction processing
  • FIG 4 shows a processing path for state of charge (SOC) estimation
  • Figure 5 shows a processing path for state of health (SOH) classification
  • Figure 6 shows a processing path for remaining useful energy state of life (RUE SOL) prediction
  • Figure 7 shows a processing path for remaining useful cycles state of life
  • Figure 8 shows a laboratory setup for a battery prognostics test bench
  • Figure 9 shows a system for battery prognostics
  • Figure 10 illustrates an ARMA model which may be used in embodiments of the present invention.
  • Figure 11 illustrates a training method for an ARMA model.
  • Figure 1 shows a schematic of a predictive diagnostic system according to an embodiment of the present invention.
  • the following example will be discussed in relation to battery diagnosis, though a similar approach may be taken towards determining the condition of fuel cells, other electrochemical cells, and other systems providing condition-related data.
  • a brief description of the system operation is provided below, with more detailed descriptions following.
  • Measurement signals are received by the diagnostic system, for example as shown at 10.
  • Measurement signals include electrical parameters such as battery voltage (V) and current (I), temperature (T), and an electrical signal (Sn) generated in response to an electrical excitation (Ex) of the battery.
  • Impedance processing 14 is used to determine battery impedance data as a function of excitation frequency.
  • the impedance data is then fitted by an electrochemical model 16, so as to provide electrochemical parameters relating to the battery.
  • a feature vector 18 comprises one or more data files generated from the measurement signals.
  • the information contained within the feature vector 18 is used by three prediction algorithms, an auto-regressive moving-average (ARMA) algorithm 20, a fuzzy logic algorithm 22, and a neural network algorithm 24.
  • Three estimation files 26, 28, and 30 are provided with estimations of SOC, SOH, and SOL by the ARMA, fuzzy logic, and neural network algorithms.
  • a decision fusion algorithm 32 determines values of SOC, SOH, and SOL from values in the estimation files.
  • the output of the decision fusion algorithm is output into a user information file 34, and is provided to a user interface 36.
  • Data may be displayed to a user using a display 38 or indicator lamps such as 40.
  • the user interface further comprises a data input mechanism 42, through which information relating to the battery can be input.
  • the measurement signals may be data sampled from an analog to digital converter receiving analog signals from an appropriate sensor.
  • the battery current (I) may be a charge or load current.
  • the temperature (T) may be an internal temperature of the battery, a surface temperature such as measured on the case or a terminal, and/or an ambient temperature measurement.
  • Measurement signals may be continuously monitored, or sampled at time intervals appropriate to the application. For example, measurement signals from a lead acid battery in a gasoline-powered vehicle may be collected at intervals of, for example, 1 - 20 minutes, 10 minutes being one specific example. Measurement signals from a battery in storage, or part of equipment in storage, may be collected at daily or weekly intervals. Measurement signals from a battery or fuel cell in an electrically powered or hybrid vehicle may be collected continuously or at intervals in the range 0.01 - 10 minutes.
  • Impedance processing 14 comprises determination of battery impedance data over a range of frequencies. The data can be processed and analyzed in the form of a Nyquist plot of impedance data, for example as illustrated in Figure 11 of U.S. Pat. No.
  • Impedance data alone were found sufficient to provide accurate diagnostics of battery condition.
  • electrical impedance data can be generated by providing a small electrical excitation current to a battery, at one or more frequencies, and receiving a signal current.
  • the excitation (Ex) and signal (Sn) electrical signals can be provided by circuitry such as described in U.S. Pat. No. 6,307,378. Other techniques such as a conventional four- wire method, can also be used.
  • the impedance data is analyzed so as to provide electrochemical parameters.
  • the provision of electrochemical parameters to the prediction algorithms allows increased accuracy, in comparison with systems where, for example, impedance data at one or more frequencies are used.
  • the frequency range of impedance determinations is preferably wide enough to allow fitting by an electrochemical model, so as to determine electrochemical parameters such as electrolyte conductivity.
  • Electrochemical models are known in the art, but have not been used previously to provide electrochemical parameters to one or more prediction algorithms. This is discussed in more detail below, in relation to Figure 3.
  • a simulated annealing algorithm was used to fit impedance data to an electrochemical model. Simulated annealing methods are well known in the mathematical arts, but have not previously been used to provide electrochemical parameters to predictive algorithms so as to determine battery condition parameters. The symmetry of electrochemical models can cause a problem with a simulated annealing algorithm, as there may be two solutions, only one of which is correct. Data obtained previously from test or training runs can be used to identify the correct solution. Modeling can be constrained to provide solutions close to earlier fittings. For example, the model can be constrained such that the solution closest to the previously correct solution is chosen, thereby avoiding selection of the other solution.
  • Collected data 60 such as measurement signals, are passed to a feature extraction processing algorithm 62 and passed to three routines, a state of charge (SOC) estimation 68, a state of health (SOH) estimation 70, and a remaining- useful-cycles state of life (RUC-SOL) prediction 72.
  • Operation information 64 is used in determining a remaining useful energy state of life (RUE-SOL) prediction 66, and also influences the remaining-useful-cycles state of life (RUC-SOL) prediction.
  • the model-based predictive diagnostics system returns five diagnostics measures (condition parameters):
  • the initial SOC which is the amount of available energy prior to discharging or after charging
  • the SOH of the battery which is a classification of the battery health in terms of the physical failure mechanisms, but could be reduced to higher level indications such as "good,” “ok,” and “bad,” and
  • the inputs to the feature extraction processing are measured observables of the monitored battery, which include (but are not limited to) terminal and cell voltage, load and charge current, ambient, surface and internal battery temperatures, and impedance excitation and sensing signals such as current waveforms.
  • each of these paths includes the feature extraction processing.
  • This processing block calibrates raw data signals and extracts features from the raw sampled data.
  • FIG. 3 shows a schematic of an example feature extraction processing method 100, which calibrates the measured voltage, current, and temperature signals and then outputs them to a feature vector.
  • the excitation and sensed current waveforms 80 are first windowed using a Blackman window 84. These signals are then passed through an FFT (Fast Fourier Transform) algorithm 86 to extract phase and magnitude information at the frequencies of interest. Voltage, current, and temperature signals 82 are calibrated using calibration algorithms 94 and the calibrated data passed to the feature vector 98. Temperature signals are passed to a heat capacity estimation algorithm 96, to provide bulk battery heat capacity data to the feature vector 98.
  • FFT Fast Fourier Transform
  • the measurement signals such as the terminal/cell voltage, load/charge current, and temperatures are fed to a calibration module, which uses stored information about each channel to insure that data is accurate in reference to collected calibration data.
  • calibrated signals are then written to the feature vector, a file that contains these calibrated signals, a time stamp, impedance data points, a heat capacity estimate, and identified electrochemical model parameters.
  • Ambient, surface, and internal temperature signals are fed into a bulk heat capacity estimator and this value saved to the feature vector.
  • the excitation signal 80 has 52 log-spaced frequencies from 1 Hz to 17.7 kHz.
  • impedance data collection may include frequencies within the ranges 1Hz - 10 KHz, 10 Hz - 10kHz, 100 Hz - 10kHz, 1 Hz - 1 KHz, 1 Hz - 100 Hz, 10 Hz - 1 kHz, or other ranges as appropriate.
  • the extracted phase and magnitude signals are then calibrated and converted to complex impedance values for each of the frequencies of interest.
  • the Blackman window 84 has better phase preservation performance than
  • the identification algorithm 92 is based on a simulated annealing search routine with enhancements to prevent parameter swapping due to model symmetries and parameter trajectory switching due to path crossings.
  • the identified parameters are then outputted to the feature vector 98. This vector is fed into the four processes that calculate the SOC, SOH, and SOL of the battery.
  • Electrochemical models which may be used are known in the art.
  • a Randies circuit can be used for the electrode-electrolyte interface process.
  • a single electrode model for cell impedance is given by: (1)
  • s j ⁇ ( ⁇ is frequency in rad/s)
  • RQ represents the electrolyte resistance
  • ⁇ represents the charge transfer resistance
  • C D L represents the double layer capacitance
  • ⁇ represents the diffusion layer coefficient
  • Z w represents the Warburg impedance.
  • the double layer capacitance is a result of the ions in the electrolyte and the electrons in the electrode waiting to participate in the chemical reactions. The build up of these charged particles results in a charged layer (i.e. capacitance).
  • the Warburg impedance is related to the mass transfer into the diffusion layer.
  • the general solution of the Equation 1 can be found in the form of a Nyquist plot, as is well known in the electrical arts.
  • the most common types of battery failures include passivation, separation, bridging, dry-out, sulfation, softening, corrosion and various mechanical failures.
  • the Randies circuit has good application not only for identifying the SOC independent of cell polarization but certain SOH failures.
  • lead-acid batteries tend to suffer from sulfation, which has shown to be associated with an increase in charge transfer resistance. Drying out of the electrolyte manifests in the Randies circuit as an increase in the ohmic resistance. Corrosion of the electrode changes the porosity of the electrode and reduces the slope of the linear leg, as is known in the art.
  • a good fit of the impedance data was found using a two-electrode, Randies circuit model including a wiring inductance.
  • Global search methods are also available for model identification such as genetic algorithms and simulated annealing.
  • genetic algorithms do not always find the global minima.
  • Simulated annealing was shown to be able to find the global minima but at the cost of many more iterations.
  • hybrid techniques There are a number of hybrid techniques available to address these issues as well.
  • a simulated annealing algorithm was used to identify model parameters. Search regions, based on the identified parameters from previous impedance measurements, were used to minimize processing iterations.
  • FIG. 4 shows a processing path for state of charge (SOC) estimation.
  • SOC state of charge
  • the SOC processing module is fed the feature vector information and outputs the initial SOC and a current estimate of the SOC if a load or charging is applied.
  • Measurement signals 120 such as terminal voltages, cell voltages, load current, charging current, ambient temperature, battery surface temperature, terminal temperature, internal battery temperature, and impedance signals) are passed to a feature extraction processing algorithm 122, which generates a feature vector 124a and a feature flag 124b.
  • the algorithm 122 may comprise one or more signal processing steps and data processing algorithms, for example as illustrated in Figure 3. Data from the feature vector is passed to three predictive algorithms: a neural network, an ARMA algorithm, and a fuzzy logic algorithm.
  • initial battery capacity state of charge (initial SOC or ISOC) estimation data is passed to a neural network ISOC predictor 128, an ARMA ISOC predictor 132, and a fuzzy logic ISOC predictor 136.
  • the three ISOC predictions (shown in Figure 4 as NN ISOC, AR ISOC, and FZ ISOC) are passed to the ISOC decision fusion algorithm 140.
  • the decision fusion algorithm provides a prediction of ISOC 144 using the predictions from the three predictive algorithms.
  • CSOC For continuous prediction of SOC during operation (CSOC), data from the feature 124a vector is passed to the neural network CSOC predictor 130, ARMA CSOC predictor 134, and the fuzzy logic CSOC predictor 138.
  • the three CSOC predictions (shown in Figure 4 as NN CSOC, AR CSOC, and FZ CSOC) are passed to the CSOC decision fusion algorithm 142.
  • the decision fusion algorithm provides a prediction of CSOC 146 using the predictions from the three predictive algorithms.
  • Measurement signals can be data sampled at intervals using an analog-to- digital converter (as indicated in Figure 4), or may comprise other data inputs of any appropriate form or origin.
  • Flags generated include the neural network ISOC prediction flag (NN I Flag), ARMA ISOC flag (AR I Flag), fuzzy logic ISOC prediction flag (FZ I Flag), corresponding flags for CSOC determinations by the three predictive algorithms (NN C flag, AR C flag, and FZ C flag), feature vector flag, and flags generated by the ISOC decision fusion algorithm 140 (DF I Flag) and CSOC decision fusion algorithm 142 (DF C Flag).
  • Flags can be used to provide error messages, confidence levels, and the like, and may be used by algorithms to provide weighting factors. In other embodiments, flags need not be generated, or only a subset of the listed flags generated.
  • ISOC and CSOC determinations can be fed back to the prediction algorithms.
  • the state of health (SOH) of the battery 126 which can include the number of previous discharge cycles and/or battery age, can also be used to assist determine ISOC using the three predictive algorithms, and within the fusion algorithms 140 and 142.
  • the initial SOC (ISOC) processing is performed by three separate algorithms, which produce separate estimations of the initial SOC (ISOC).
  • Neural network, auto-regressive moving-average (ARMA), and fuzzy logic algorithms are trained and used to perform the estimations. These three estimates are fed into a decision fusion algorithm that weights the estimates based on a confidence measure.
  • the confidence measure uses information about the SOC algorithms, previous performance, etc.
  • the initial SOC will change based on load or charging method, so this estimation is updated continuously to account for changes in the loading or charging.
  • neural network For estimation of the most recent SOC (continuous SOC, or CSOC), neural network, ARMA, and fuzzy logic algorithms are used and produce three separate estimations of the most recent SOC.
  • This processing stage uses the feature vector information and initial SOC estimation from the decision fusion process to make the estimations.
  • the three estimations are fed into a decision fusion algorithm 142 that weights the SOC estimates based on a confidence similar to the decision fusion processing for the initial SOC.
  • the neural network, ARMA, fuzzy logic, and decision fusion processing algorithms are updated based on SOH information fed in from the SOH classification-processing path.
  • Figure 5 shows a processing path for state of health (SOH) classification.
  • Measurement signals 160 comprising measurement signals such as terminal voltages, cell voltages, load current, charging current, ambient temperature, battery surface temperature, terminal temperature, internal battery temperature, and impedance signals is received and passed to one or more feature extraction processing algorithms, for example as illustrated in Figure 3.
  • the algorithm 162 generates a feature vector 164a and a feature flag 164b.
  • the information contained in the feature vector 164a is used by three prediction algorithms, a neural network SOH classifier 166, a linear/statistical SOH classifier 168, and a fuzzy logic SOH classifier 170.
  • the outputs of the three prediction algorithms, a prediction of the SOH and a flag are passed to a SOH decision fusion algorithm 172.
  • the decision fusion algorithm 172 also receives information 174 related to cycle SOC, for example initial, present, and historical values.
  • the decision fusion algorithm produces an SOH (DF SOH) prediction and a decision fusion SOH flag (DF H Flag).
  • the present condition parameter (battery SOH) is presented to the user (176).
  • the SOH processing flow uses the feature vector information to classify the physical condition of the battery. As with the SOC estimation processing, three separate algorithms are used to classify the current health of the battery. The classification segregation is based on failure mechanism. The three classifications are fed into a decision fusion-processing block. The output of the fusion processing is a refined classification based on classification agreement, previous performance of each of the classifiers, etc. The SOH processing can provide this information to the user/interface as well as be used to update SOC estimation processing and SOL prediction for remaining recharging life.
  • Figure 6 shows a processing path for remaining useful energy state of life (RUE SOL) prediction.
  • Information for example derived from measurement signals and other processing steps as described in more detail elsewhere, is passed to three prediction algorithms.
  • the information comprises load and temperature profiles 180, continuous prediction of SOC during operation (CSOC) 182, and initial battery capacity SOC (ISOC) 184.
  • the three algorithms are a neural network (NN) RUE predictor 186, an ARMA RUE predictor 188, and a fuzzy logic (FZ) RUE predictor 190.
  • the NN predictor 186 produces an NN SOL prediction
  • the ARMA RUE predictor 188 produces an AR SOL prediction
  • the FZ RUE predictor 190 produces an FZ SOL prediction.
  • the three predictions are passed to a RUE decision fusion algorithm 192, which produces a decision fusion (DF) prediction of RUE (DF RUE prediction), which is then used to determine how long before the battery cut-off 196.
  • DF decision fusion
  • the fusion algorithm 192 also receives battery state of health (SOH) data 194, which can be used to assist determination of RUE. For example, as state of health degrades over time or battery cycles, different weights can be given to the prediction algorithm outputs. The appropriate weights can be determined in a training step.
  • SOH battery state of health
  • This particular branch of the processing provides the user/interface with a prediction of the remaining time in the discharge or charge cycle.
  • This processing branch uses the initial and continuous SOC information from the SOC processing branch along with loading/charging and temperature profiles to make a prediction on the remaining time left in the cycle.
  • the three-prong separate prediction algorithm approach is used in this branch as well. Neural network, ARMA, and fuzzy logic algorithms are employed to make the three separate predictions. These predictions are then fed into a decision fusion-processing block where they are weighted based on a confidence measure.
  • Figure 7 shows the RUC SOL prediction-processing path. This branch of the processing predicts the remaining number of recharges.
  • the three-prong prediction algorithm approach model is used in this branch as well.
  • the prediction models are updated or modified based on SOH classification. Since different failure mechanisms age the battery at different rates, using a single prediction model would limit performance. For example, corrosion will age the battery at a different rate than passivation and this translates to a different end of life point. Also, more than one failure mechanism may be aging the battery and prediction performance will improve as one of the failure mechanisms begins to dominant the health of the battery.
  • Information 200 comprising measurement signals such as terminal voltages, cell voltages, load current, charging current, ambient temperature, battery surface temperature, terminal temperature, internal battery temperature, and impedance signals is received and passed to a feature extraction processing algorithm 202, for example as illustrated in Figure 3.
  • the feature vector 204a provides information for the three prediction algorithms: the neural network RUC predictor 208, the ARMA RUC predictor 210, and the fuzzy logic RUC predictor 212.
  • SOH classification information 206 is also provided to the three algorithms.
  • the three algorithms each produce a RUC prediction and flag.
  • the three RUC predictions are passed to the RUC decision fusion algorithm 214, which produces a RUC prediction (DF RUC) and a flag.
  • the RUC prediction is used to determine the number of remaining battery recharges 216.
  • a method for processing measured electrochemical monitored signals comprising the steps, executed by a computer comprises using a feature extraction processing algorithm to generate complex impedance values, electrochemical model parameters, calibrated and time stamped voltage signals, calibrated and time stamped current signals, calibrated and time stamped temperature signals, and information regarding bulk battery heat capacity; and transferring the information generated by the feature extraction processing algorithm to a remaining useful energy state-of-life predictor, a state-of-charge estimator, a state-of-health classifier and a remaining useful cycle state-of-life predictor, thereby generating a measurement of the time period remaining until battery depletion, a measurement of initial battery state-of- charge, a measurement of battery state-of-charge during operation, a measurement of battery state-of-health and a measurement of the number of remaining battery recharges.
  • the electrochemical monitored signals may comprise terminal voltage, cell voltage, load current, charging current, ambient temperature, battery surface temperature, terminal temperature, internal battery temperature and impedance excitation and response.
  • the information generated by the feature extraction processing algorithm may be
  • An improved electrochemical signal processing system comprises means for storing electrochemical monitored signals, means for generating a database of complex impedance values using feature extraction processing; and means for transferring information generated by feature extraction processing to a state-of-life predictor, a state-of-charge estimator and a state-of-health classifier.
  • the system may further comprise a battery and a digital user interface.
  • the feature extraction processing algorithm may be run using only the impedance data as an input.
  • the voltage, current, and temperature data is not required.
  • other subsets of the inputs discussed hereinabove may be used as inputs to the feature extraction processor.
  • the data supplied to the feature vector files may be a subset of the data discussed hereinabove.
  • Figure 8 shows an example laboratory setup that was designed to run batteries under prescribed load/charge and temperature conditions, and provides a laboratory setup for a battery prognostics test bench. This should be considered only an example, since not all portions are necessary, or even preferred, for the practice of the present invention (for example, the use of a temperature chamber and an electronic load are not required for some applications).
  • the invention could alternatively be implemented on a PC or an embedded system.
  • the system comprises a computer 220, power supply 222, temperature chamber 224, battery under test 226, electronic load 228, signal conditioning hardware 230 for terminal voltage, current, and thermocouples, an impedance box 243, and second signal conditioning hardware 236 for the impedance box 234.
  • the description of the laboratory setup can be divided into three sections: control of conditions, signal measurement and conditioning, and data sampling and collection.
  • the two main controls for running a battery test are the load/charging and temperature of the battery, which are the key influences on available battery charge and life.
  • An electronic load 228 was used to discharge the batteries and is controlled via an RS-232 connection to the workstation PC 220.
  • the electronic load is capable of constant resistance (CR), constant current (CC), constant voltage (CV), and constant power (CP) loading.
  • a variable power supply 222 was used and is capable of charging under constant voltage (CV) or constant current (CC) conditions.
  • the power supply is controlled via an RS-232 connection to the workstation PC 220.
  • a temperature chamber 224 was used to test batteries from -20°C to 150°C and is controlled by the workstation PC via RS-232 serial interface.
  • the measurement signals for battery diagnostics included: cell and terminal voltage, load and charging current, ambient, case surface, and internal cell temperatures, electrolyte pH, and wideband electrical impedance.
  • signal conditioning hardware 230 was selected that could handle these different types of measurements.
  • the National Instruments SCXI-based signal conditional equipment was selected since it could handle voltage, current, and thermocouple signals over a wide range and was modular for easy configuration and modifications.
  • the bandwidth for this signal condition hardware was set at 4 Hz, which was more than sufficient for the voltage, current, and temperature signals.
  • Impedance measurements were made using the methods described in U.S. Patent No. 6,307,378. An AC ground circuit was used to reduce the required voltage rating (and subsequent physical size) of the DC blocking capacitor.
  • the impedance measurement hardware 232 produces two signals for the impedance and each channel has a bandwidth of 20kHz, which is a much higher sampling requirement than the other signals measured on the battery.
  • the analog signals were digitally sampled using two data acquisition (DAQ) boards installed into the workstation PC 220.
  • the first of the two DAQ boards was used to control the SCXI hardware and sample the voltage, current, thermocouple, and pH signals at a rate of 10 sample/s.
  • the second DAQ board was used to sample the two signals from the impedance measurement hardware box and sampled these signals at a rate of 5,000 samples/s and 200,000 samples/s (based on interrogation waveform bandwidth).
  • Data sampling was done in 10 windows in 1-minute intervals and each data sampling for each signal was saved as an individual file. Having the data partitioned in the manner is less susceptible to corruption than if the all the data is saved as one large file.
  • test runs were design to cover those conditions that predominantly affect the battery state.
  • the four main factors considered for test design were: 1) operating temperature, 2) loading/charging current, 3) battery chemistry, and 4) capacity size.
  • Test runs were conducted under the following procedure: 1. A battery chemistry and size was selected for the run series and the type of measurements for that battery were determined (e.g. terminal voltage, surface temperature, etc.). 2. The loading, charging, and temperature profiles were selected and a schedule for running the test was drawn up.
  • test cycle was then initiated under the following test conditions: a. If the test battery was a primary battery, the battery was discharged until the cutoff voltage was reached and "no-load” measurements were taken once the terminal voltage of the battery reached a steady-state level (in addition to the measurements taken online during discharge). b. If the test battery was a secondary battery, after discharge and "no-load” measurements, the battery was charged and measurements were taking online during the charging and after charging.
  • the feature extraction processing software was used to generate a Feature Vector file and was saved with the archived test run data.
  • Figure 9 illustrates a portable system that could be taken into the field to test a battery 244 (for example in vehicles and equipment), comprising a laptop computer 240 and an impedance measurement box 242.
  • a self-contained apparatus was also constructed, having a housing with dimensions of approximately 2" x 4" x 1.5".
  • the housing contains a processor, memory, data input mechanism (for receiving identification data relating to a battery under test), a pair of electrical connectors to connect to the battery under test, battery impedance measurement circuitry, impedance data processing circuitry, and a display.
  • Software executed by the processor, was operable to provide a fuzzy logic prediction algorithm, an ARMA prediction algorithm, a neural network prediction algorithm, and a decision fusion algorithm.
  • the device was operable to determine battery impedance over a range of frequencies, extract electrochemical parameters from the impedance data, provide information comprising the electrochemical parameters to three prediction algorithms (as described in detail above), and determine battery conditions by passing the outputs of the three prediction algorithms to a decision fusion algorithm.
  • An analog-to-digital converter can be used to convert analog signals (such as terminal voltage) to digital signals.
  • the only measurement signal received by the device related to battery impedance.
  • the device provided an excitation signal to the battery through electrical contacts in electrical communication with the battery.
  • An apparatus can be trained on a specific battery.
  • a user enters a battery model number (for example, a brand name and any other product identification number), and training files corresponding to that model are used in predicting required battery conditions.
  • files for a similar battery may be used, for example a battery of similar chemistry and charge capacity.
  • the product identifier, vehicle identifier, or similar identifier from a device, vehicle, or other equipment containing the battery may be used to identify the battery and call up the appropriate training files.
  • the decision fusion algorithm may keep learning as the algorithm is used, so that data under certain conditions is deweighted.
  • Training files may comprise data collected in relation to a specific cell, or class or model of cell, and used later by prediction and/or decision fusion algorithms to improve accuracy.
  • a device to assist with battery diagnostics may be a stand-alone unit, receiving signals from a battery and communicating with a portable computing device so as to use the display capabilities and processing power of the computing device.
  • a device may take the form of an accessory within, connected to, or otherwise in communication with a host electronic device, for example a card inserted into a computer.
  • Figure 10 illustrates an ARMA model which may be used in embodiments of the present invention.
  • ARMA models are commonly used for system identification because they are linear and easy to implement, and complement the more complex models (neural network and fuzzy logic) being used.
  • a second order model was sufficient to predict SOC.
  • the model (illustrated in Figure 10) is represented by the equation, with y
  • y(t) a X(t) + b X(t-l) + c 0 y(t-l) (2) representing SOC, X representing a vector of model inputs, and a, b, and c 0 representing the model coefficients (determined from the LS (least squares) fit during ARMA training).
  • Measured impedance data can used in the model. These variables represent the electrochemical processes occurring inside the battery during its discharge and are dependent on the amount of charge remaining in the battery.
  • the electrolyte resistance (R ⁇ ), for example, is representative of the amount of electrolyte that is available for reaction. The lower the amount of electrolyte, the less available capacity there is remaining in the battery.
  • the charge transfer resistance ( ⁇ ) represents the amount of plate surface area that is available for reaction. This value decreases as the SOC decreases.
  • the double layer capacitance (C DL ) represents the number of ions that are waiting to react in the battery. This value increases as the amount of available capacity decreases due in part to the diminishing amount of electrolyte and plate surface area.
  • Preconditioning made training of the model more effective by creating inputs with consistent behavior, regardless of battery conditions.
  • the derivative of each input can be made prior to entry into the model.
  • all of the model inputs may have a similar shape when plotted against SOC. Because of the possible wide range of values of the inputs, normalization of the parameters prior to entry into the model may be helpful. This allows the model coefficients to be similar in size and helps eliminate one input from dominating the model. For example, each input can be normalized with regards to the minimum and maximum values of the training set.
  • the SOC from the previous prediction can be used in order to make a new SOC prediction. This creates a problem when making the first prediction, however, because the initial SOC of the battery is unknown. Assuming the battery always begins with 100% SOC may not be efficient if this value is dependent on such things as manufacturing and shelf life. Therefore, the longer a battery sits without being used, the more charge is lost and its initial SOC is diminished. Also, charging efficiency in secondary cells causes a variation in initial battery capacity. In addition, a battery may have been partially discharged prior to use. No load SOC prediction methods may be used, which use impedance measurements that are taken before the load is applied to the battery. There is a relationship between these "No Load Condition" measurements and the amount of capacity (or SOC) that is available in the battery.
  • Figure 11 illustrates training of the ARMA model.
  • the ARMA model may be trained in order to use the ARMA model to make a SOC prediction. This can be done by selecting a training set of data from a completed cycle. Because the entire set of data is available, the endpoint of the cycle is known and the actual SOC of the battery at each point can be calculated. The model then uses a LS fit to calculate the model coefficients that enable the inputs to result in the actual SOC. These model coefficients will then be used for each successive run to calculate the SOC.
  • Modeling of secondary cells can differ from modeling of primary cells because of health effects on a battery's SOC. As a battery's health diminishes, its initial SOC and internal impedance decrease. In order to account for this, secondary cells use a recursive training routine in which the model is retrained after each cycle to be used for the prediction of the next cycle. This helps eliminate the effects of SOL and the changing impedance of the battery as its health diminishes.
  • neural networks are well known in the computing and mathematical arts, and will not be described further here.
  • neural networks designed for direct SOC estimation use one hidden layer and were trained with the backpropagation gradient decent learning algorithm using supervised learning.
  • the backpropagation algorithm calculates the gradient of the error between the network output and target with respect to the network weights and then adjusts the weights in the direction of steepest decent. As the process is repeated over many epochs or iterations, the weights move towards a location of minimum output error.
  • Network training is terminated when a stopping criterion such as a minimum error value or maximum number of training epochs is reached.
  • Preprocessing techniques similar to those discussed in the ARMA section proved to be effective for the neural network models as well.
  • the features were passed through a lowpass Butterworth filter to remove high frequency noise from the model fitting routine.
  • the gradient of the features was taken with respect to time in order to take advantage of the fact that the features were similar in shape but often were offset in value.
  • this gradient operation often produces signal spikes orders of magnitude larger than the average signal value.
  • the feature signals are normalized with respect to the maximum and average values of the training set features so that they fall in the range of -1 to +1 if tansigmoidal transfer functions are used or from 0 to 1 for logsigmoidal functions.
  • Smaller networks also tend to be better at generalization.
  • the selection of the number of delays and the length of the delays is crucial to the performance of the networks. Both short and long delays were tried during different training runs. The short delays may give better performance, indicating that the battery SOC does not involve long time-constants.
  • a neural network trainer can be used to construct a set of rules from available data collected from one or more batteries. Where a number of measurement signals were available (for example, 6), it was sometimes found advantageous to supply a sub-set of the available data within the feature vector to the fuzzy logic model, so as reduce the number of fuzzy rules generated by the neural trainer.
  • Decision fusion can be used to improve the quality of condition assessment and increase the confidence of the assessment.
  • Algorithms are known in the art, but have not been previously used to determine battery condition parameters from the outputs of a plurality of predictive algorithms. For example, SOC, SOH, and SOL estimates from three predictive algorithms provide three parallel estimates of each of these condition parameters. These estimates are fed into a decision fusion algorithm that determines how well the predictors compare, and has access to processed sensor data, previous history, and knowledge about the battery type. Using this information, the decision fusion algorithm provides a combined prediction of the condition parameter (SOC, SOH, or SOL) with a measure of confidence.
  • three SOC predictions were fed to the decision fusion algorithm; 85%, 83%, and 30%.
  • the decision fusion algorithm also retrieved the SOC information from the previous cycle and battery type information.
  • the algorithm then decided that the two SOC predictions, 85% and 83% are more likely to be correct than the other, not only because they agree with each other but because the previous cycle SOC was more similar to these estimates under the current operating conditions.
  • the 30% SOC prediction is then de-weighted by the algorithm, a single SOC prediction is calculated, and a confidence is assigned to the new SOC estimate.
  • the decision fusion algorithm may also have access to the sensor signals that are fed to the SOC, SOH, and SOL predictors.
  • a dead sensor signal may have caused the bad 30% SOC prediction. If the other two
  • Implementation of the algorithms described above may be in the form of a software program executable by a processor within a device according to an embodiment of the present invention.
  • the estimates provided by the predictive algorithms may be averaged, or combined according to predetermined weights, or combined using any convenient method.
  • Embodiments of the present invention may be used in commercial markets such as automotive batteries, electric vehicle batteries, and backup power systems for communication, banking and computer networks, aircraft and sea vessel battery systems, small vehicle and equipment batteries found in forklifts, night vision goggles, and radios.
  • FUEL CELLS Embodiments of the present invention can be used with fuel cells. Fuel cells do not have to be monitored for SOC, but SOH (i.e. conversion efficiency) is an important issue for operational readiness and overall life.
  • the porous gas-diffusion electrodes of a fuel cell are under mixed control of electrode kinetics, mass transfer and ionic conduction; therefore, the rate-limiting process cannot be described in simple terms.
  • Contact resistance and ohmic resistance are key parameters that depend strongly on the specific design and operating conditions of each cell.
  • In situ impedance methods are very desirable to characterize the rate-limiting processes in fuel cells. AC impedance measurements may be useful for achieving such characterization.
  • the ionic resistance of a solid polymer electrolyte membrane can be studied using AC impedance. Also, dehydration of the membrane reduces the ionic conductivity and is itself affected by current passage. The diffusion of water in the membrane can be studied as well.
  • the membrane resistance can be identified by means of an electric circuit model (similar to the Randies circuit for battery cells) with grain boundary resistance and capacitance representing a "membrane relaxation" process related to membrane dehydration, bulk membrane resistance, and contact resistance.
  • Modeling polymer electrolyte membrane fuel cell (PEMFC) electrode response can be achieved with a porous electrode model incorporating a transmission line network.
  • the model assumes that part of the pore is covered with a thin film and part of it contacts a flooded agglomerate.
  • PEMFC operate at high efficiencies when using pure hydrogen, but fail when using hydrogen obtained from hydrocarbon or methanol processing. This is due to electrode poisoning from CO entering the fuel cell.
  • Adsorbed CO not only affects the reactivity of the accessible electrode surface by preventing H 2 adsorption by site exclusion, but also lowers the reactivity of the remaining uncovered sites through dipole interactions and electron captures. The amount of CO contamination can be observed using impedance measurements thus making it possible to established H 2 flushing control when the CO contamination gets too high (i.e. diminishing the cell efficiency).
  • Semi-fuel cells such as aluminum/hydrogen peroxide semi-fuel cells
  • Semi-fuel cells may be used for e.g. underwater electric vehicles.
  • the parasitic homogeneous self-decomposition of the hydrogen peroxide 3) the parasitic homogeneous self-decomposition of the hydrogen peroxide, and 4) the heterogeneous decomposition of the hydrogen peroxide with substrate materials, such as the nickel substrate, silver catalyst or palladium/iridium catalyst.
  • embodiments of the present invention can be used to evaluate fuel cell systems, and hybrid systems including a fuel cell.
  • CBM Condition-Based Maintenance
  • RUL remaining useful life
  • the philosophy is in contrast to performing maintenance on a time/use basis or corrective maintenance based on the occurrence of a failure.
  • the CBM approach if successfully implemented, provides the promise of reduced life cycle maintenance costs, improved safety, and increased operational readiness.
  • Maintenance actions can be performed when a component or system fails (corrective), on an event or time basis (preventative), or when an assessment of condition indicates that a failure is likely (predictive).
  • Corrective maintenance produces low maintenance cost (minimal preventative actions), but high performance costs caused by operational failures.
  • preventative maintenance practice produces low operations costs, however more preventative actions produce greater maintenance department costs.
  • application of statistical safe-life methods usually leads to very conservative estimates of the probability of failure. The result of such methods is an additional hidden cost associated with disposing of components that still retain significant remaining useful life.
  • a model-based predictive diagnostics system for primary and secondary batteries can form part of a condition-based management system.
  • Embodiments of the present invention can be used to evaluate other systems comprising a conducting component.
  • a conducting component is in machine maintenance, in particular in machine oil quality analysis.
  • Machine oil is an ionic compound and will conduct electricity based on changes in concentration, additives, and contaminates such as water and debris. Applying the impedance measurement approach and diagnostics processing to oil quality can lead to improved machine maintenance.
  • Embodiments of the present invention can also be used to monitor the state of capacitive systems, such as supercapacitors, and hybrid systems including an electrochemical cell and a supercapacitor.

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Abstract

Procédé pour déterminer un paramètre d'état d'une cellule électrochimique, telle qu'une batterie, qui comporte les étapes consistant à obtenir des données d'état corrélées avec le paramètre d'état ; à fournir les données d'état à une pluralité d'algorithmes de prédiction, chaque algorithme produisant une estimation d'un paramètre d'état, de sorte qu'une pluralité d'estimations de paramètres d'état sont produites ; à déterminer le paramètre d'état à l'aide de la pluralité d'estimations de paramètre d'état.
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WO2003098768A2 (fr) * 2002-05-17 2003-11-27 Green Light Power Technologies , Inc. Procede et systeme de verification, de calibrage et de simulation d'un poste d'essai de piles a combustible
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